Preprocessing of FMRI Data

fMRI Graduate Course

October 23, 2002

What is preprocessing?

• Correcting for non-task-related variability in experimental data– Usually done without consideration of

experimental design; thus, pre-analysis– Occasionally called post-processing, in

reference to being after acquisition

• Attempts to remove, rather than model, data variability

Signal, noise, and the General Linear Model

MYMeasured Data

Amplitude (solve for)

Design Model

Noise

Cf. Boynton et al., 1996

Signal-Noise-Ratio (SNR)

Task-Related Variability

Non-task-related Variability

Preprocessing Steps

• Slice Timing Correction

• Motion Correction

• Coregistration

• Normalization

• Spatial Smoothing

• Segmentation

• Region of Interest Identification

Tools for Preprocessing

• SPM

• Brain Voyager

• VoxBo

• AFNI

• Custom BIAC scripts (Favorini, McKeown)

Slice Timing Correction

Why do we correct for slice timing?

• Corrects for differences in acquisition time within a TR– Especially important for long TRs (where expected HDR

amplitude may vary significantly)– Accuracy of interpolation also decreases with increasing TR

• When should it be done?– Before motion correction: interpolates data from (potentially)

different voxels• Better for interleaved acquisition

– After motion correction: changes in slice of voxels results in changes in time within TR

• Better for sequential acquisition

Effects of uncorrected slice timing

• Base Hemodynamic Response

• Base HDR + Noise

• Base HDR + Slice Timing Errors

• Base HDR + Noise + Slice Timing Errors

Base HDR: 2s TR

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r = 0.85r = 0.92

r = 0.62

HDR + Noise + Slice Timing

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Interpolation Strategies

• Linear interpolation

• Spline interpolation

• Sinc interpolation

Motion Correction

Head Motion: Good and Bad

Correcting Head Motion

• Rigid body transformation– 6 parameters: 3 translation, 3 rotation

• Minimization of some cost function– E.g., sum of squared differences

Simulated Head Motion

Severe Head Motion: Simulation

Two 4s movements of 8mm in -Y direction (during task epochs)

Motion

Severe Head Motion: Real Data

Two 4s movements of 8mm in –Y direction (during task epochs)

Motion

Effects of Head Motion Correction

Limitations of Motion Correction

• Artifact-related limitations– Loss of data at edges of imaging volume– Ghosts in image do not change in same manner as

real data

• Distortions in fMRI images– Distortions may be dependent on position in field, not

position in head

• Intrinsic problems with correction of both slice timing and head motion

Coregistration

Should you Coregister?

• Advantages– Aids in normalization– Allows display of activation on anatomical images– Allows comparison across modalities– Necessary if no coplanar anatomical images

• Disadvantages– May severely distort functional data– May reduce correspondence between functional and

anatomical images

Normalization

Standardized Spaces

• Talairach space (proportional grid system)– From atlas of Talairach and Tournoux (1988)– Based on single subject (60y, Female, Cadaver)– Single hemisphere– Related to Brodmann coordinates

• Montreal Neurological Institute (MNI) space– Combination of many MRI scans on normal controls

• All right-handed subjects– Approximated to Talaraich space

• Slightly larger• Taller from AC to top by 5mm; deeper from AC to bottom by 10mm

– Used by SPM, National fMRI Database, International Consortium for Brain Mapping

Normalization to Template

Normalization Template Normalized Data

Anterior and Posterior Commissures

Anterior Commissure

Posterior Commissure

Should you normalize?

• Advantages– Allows generalization of results to larger population– Improves comparison with other studies– Provides coordinate space for reporting results– Enables averaging across subjects

• Disadvantages– Reduces spatial resolution– May reduce activation strength by subject averaging– Time consuming, potentially problematic

• Doing bad normalization is much worse than not normalizing

Slice-Based Normalization

Before Adjustment (15 Subjects)

After Adjustment to Reference Image

Registration courtesy Dr. Martin McKeown (BIAC)

Spatial Smoothing

Techniques for Smoothing

• Application of Gaussian kernel– Usually expressed in

#mm FWHM– “Full Width – Half

Maximum”– Typically ~2 times

voxel size

Effects of Smoothing on Activity

Unsmoothed Data

Smoothed Data (kernel width 5 voxels)

Should you spatially smooth?

• Advantages– Increases Signal to Noise Ratio (SNR)

• Matched Filter Theorem: Maximum increase in SNR by filter with same shape/size as signal

– Reduces number of comparisons• Allows application of Gaussian Field Theory

– May improve comparisons across subjects• Signal may be spread widely across cortex, due to intersubject

variability

• Disadvantages– Reduces spatial resolution – Challenging to smooth accurately if size/shape of signal is not

known

Segmentation

• Classifies voxels within an image into different anatomical divisions– Gray Matter– White Matter– Cerebro-spinal Fluid (CSF)

Image courtesy J. Bizzell & A. Belger

Histogram of Voxel Intensities

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Region of Interest Drawing

Why use an ROI-based approach?

• Allows direct, unbiased measurement of activity in an anatomical region– Assumes functional divisions tend to follow

anatomical divisions

• Improves ability to identify topographic changes– Motor mapping (central sulcus)– Social perception mapping (superior temporal sulcus)

• Complements voxel-based analyses

Drawing ROIs

• Drawing Tools– BIAC software (e.g., Overlay2)– Analyze– IRIS/SNAP (G. Gerig)

• Reference Works– Print atlases– Online atlases

• Analysis Tools– roi_analysis_script.m

ROI Examples

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Distance Posterior from the Anterior Commissure (in mm)

Left Hemisphere - Gaze Shifts Right Hemisphere - Gaze Shifts

60 55 50 45 40 35 30 25 20 15 10 5 0

BIAC is studying biological motion and social perception – here by determining how context modulates brain activity in elicited when a subject watches a character shift gaze toward or away from a target.

Additional Resources

• SPM website– Course Notes

• http://www.fil.ion.ucl.ac.uk/spm/course/notes01.html

– Instructions

• Brain viewers– http://www.bic.mni.mcgill.ca/cgi/icbm_view/